if(!require("reticulate")) install.packages("reticulate")Loading required package: reticulateif(!require("quarto")) install.packages("quarto")Loading required package: quartolibrary(reticulate)
use_python("/Users/MuzDog/anaconda3/bin/python3")A continuación se presenta el cuaderno con los graficos creados para el 30DayChartChallenge propuesto como actividad en la asignatura de Visualizacion de Datos del master de Big Data de la universidad internacional de Valencia
knitr::include_graphics("prompts.png")
knitr::include_url("https://datawrapper.dwcdn.net/2qtb7/1/", height = 800)import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.image as mpimg
data = {
'Franchise': ['The Matrix', 'The Matrix', 'The Matrix', 'The Matrix',
'John Wick', 'John Wick', 'John Wick', 'John Wick'],
'Movie': ['1', '2', '3', '4', '1', '2', '3', '4'],
'Rating': [7.9, 6.4, 6.0, 4.9, 6.4, 6.4, 6.4, 6.7]
}
df = pd.DataFrame(data)
labels = ['1', '2', '3', '4']
x = np.arange(len(labels))
fig, ax = plt.subplots(figsize=(10,6))
matrix_ratings = df[df['Franchise'] == 'The Matrix']['Rating']
wick_ratings = df[df['Franchise'] == 'John Wick']['Rating']
bars1 = ax.bar(x - 0.3/2, matrix_ratings, width = 0.3, label='The Matrix', color='#6A67CE')
bars2 = ax.bar(x + 0.3/2, wick_ratings, width = 0.3, label='John Wick', color='#FF6F61')
matrix_trend = np.polyfit(x, matrix_ratings, 1)
matrix_trend_line = np.polyval(matrix_trend, x)
wick_trend = np.polyfit(x, wick_ratings, 1)
wick_trend_line = np.polyval(wick_trend, x)
ax.plot(x, matrix_trend_line, color='darkviolet', linestyle='--', label='Tendencia The Matrix')
ax.plot(x, wick_trend_line, color='darkred', linestyle='--', label='Tendencia John Wick')
ax.set_xlabel('Película')
ax.set_ylabel('Rating')
ax.set_title('Comparación de Ratings entre The Matrix y John Wick')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend( loc='upper center', ncol = 2)
plt.figtext(0.5, -0.05, "Gráfico: Mario Salvador López Muñoz - Fuente: Filmaffinity - Creado con python y ChatGPT",
wrap=True, horizontalalignment='center', fontsize=10, style='italic', color='gray')
plt.show()
# Libraries
library(ggbump)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(ggplot2)
library(ggrepel)
# Datos de ejemplo
data <- data.frame(
Franchise = c('The Matrix', 'The Matrix', 'The Matrix', 'The Matrix',
'John Wick', 'John Wick', 'John Wick', 'John Wick'),
Movie = c('1', '2', '3', '4', '1', '2', '3', '4'),
Rating_filmaffinity = c(7.9, 6.4, 6.0, 4.9, 6.4, 6.4, 6.4, 6.7),
Rating_IMDB = c(8.7, 7.2, 6.7, 5.6, 7.4, 7.4, 7.4, 7.7),
Name = c("The Matrix \n(1999)",
"The Matrix \nReloaded (2003)",
"The Matrix \nRevolutions (2003)",
"The Matrix \nResurrections (2021)",
"John Wick \nOtro día para matar \n(2014)",
"John Wick: Chapter 2 \n(2017)",
"John Wick: Chapter 3 \nParabellum \n(2019)",
"John Wick: Capítulo 4 \n(2023)")
)
data$Rating_filmaffinity <- as.numeric(data$Rating_filmaffinity)
data$Rating_IMDB <- as.numeric(data$Rating_IMDB)
data$Franchise <- factor(data$Franchise, levels = c('The Matrix', 'John Wick'))
data$Movie <- as.integer(data$Movie, levels = c('1', '2', '3', '4'))
#GRafico
ggplot(data, aes(x = Movie, y = Rating_filmaffinity, color = Franchise)) +
geom_bump(size = 2) +
geom_point(size = 6) +
geom_text_repel(aes(label = Name), nudge_y = 0.2, fontface = "bold", size=3, color = "grey60") +
theme_minimal() +
labs(
title = "Rating de peliculas donde aparece Neo \n aunque no sea Neo",
x = "Pelicula en la serie",
y = "Rating"
) +
theme(
plot.title = element_text(
family = "sans",
size = 20,
face = "bold",
color = "#000000"
))Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
# Load packages
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ lubridate 1.9.3 ✔ tibble 3.2.1
✔ purrr 1.0.2 ✔ tidyr 1.3.1
✔ readr 2.1.5
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(fuzzyjoin)
library(ggstream)
library(colorspace)
library(ggtext)
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:lubridate':
stamplibrary(tidyr)
library(ggplot2)
library(dplyr)
library(gridExtra)
Attaching package: 'gridExtra'
The following object is masked from 'package:dplyr':
combine#Importación de los datos
#setwd("/Users/MuzDog/Desktop/MasterBigData/0_Estadistica_Adv/Actividad_2")
data <- read_csv("/Users/MuzDog/Desktop/MasterBigData/Estadistica_Adv/Actividad_2/export (1).csv")Rows: 3219 Columns: 11
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (8): tavg, tmin, tmax, prcp, snow, wdir, wspd, pres
lgl (2): wpgt, tsun
date (1): date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data$date <- as.Date(data$date, format="%Y-%m")
#Preprocesamiento (Limpieza filas y columnas vacias):
data_clean <- data %>% drop_na(tavg, tmin, tmax) # Limpieza filas vacias
data_clean <- data_clean %>% select(-snow, -wdir, -wspd, -wpgt, -pres, -tsun) # Limpieza columnas vacias
data_clean$AñoMes <- format(data_clean$date, "%Y-%m") # Crear una nueva columna que solo tenga el año y el mes
data_mensual <- data_clean %>% # Calcular la temperatura media para cada mes
group_by(AñoMes) %>%
summarise(Temp_Media_Mensual = mean(tavg, na.rm = TRUE), Temp_Minima_Mensual = mean(tmin, na.rm = TRUE), Temp_Maxima_Mensual = mean(tmax, na.rm = TRUE))
data_mensual$AñoMes <- as.Date(paste0(data_mensual$AñoMes, "-01"))
# Visualizacion inicial
plot1 <- ggplot(data_clean, aes(x = date)) +
geom_line(aes(y = tmax, color = "Tmax")) +
geom_line(aes(y = tmin, color = "Tmin")) +
geom_line(aes(y = tavg, color = "Tavg")) +
labs(title = "Variables Meteorológicas en Madrid (2015-2024)",
x = "Fecha", y = "Valor") +
scale_color_manual(values = c("Tmax" = "red", "Tmin" = "green", "Tavg" = "yellow")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
#Makeover
# Crear eñ cpnjunto de datos a partir del anterior.(Paso innecesario pero me da pereza cambiar todo)
x <- data_clean$date
y1 <- data_clean$tmin
y2 <- data_clean$tavg
y3 <- data_clean$tmax
data <- data.frame(x, y1, y2, y3)
# Encontrar el valor mínimo y sus coordenadas
min_tmin <- min(data$y1, na.rm = TRUE)
min_tmin_x <- data$x[which.min(data$y1)]
# Crear el gráfico
plot2 <- ggplot() +
geom_line(aes(x = x, y = y1, color = "T Minima (ºC)"), data = data, linewidth = 0.7) +
geom_line(aes(x = x, y = y2, color = "T Media (ºC)"), data = data, linewidth = 0.7) +
geom_line(aes(x = x, y = y3, color = "T Maxima (ºC)"), data = data, linewidth = 0.7) +
geom_line(aes(x = AñoMes, y = Temp_Minima_Mensual, color = "blue"), data = data_mensual, linewidth = 0.1) +
geom_line(aes(x = AñoMes, y = Temp_Media_Mensual, color = "T Media Mensual (ºC)"), data = data_mensual, linewidth = 0.7) +
geom_line(aes(x = AñoMes, y = Temp_Maxima_Mensual, color = "darkmagenta"), data = data_mensual, linewidth = 0.1) +
# Colorear el área entre y1 y y2
geom_ribbon(aes(x = AñoMes, ymin = pmin(Temp_Minima_Mensual, Temp_Media_Mensual), ymax = pmax(Temp_Minima_Mensual, Temp_Media_Mensual),
fill = "Área T Min - T Media"), alpha = 0.5 , data = data_mensual) +
# Colorear el área entre y2 y y3
geom_ribbon(aes(x = AñoMes, ymin = pmin(Temp_Media_Mensual, Temp_Maxima_Mensual), ymax = pmax(Temp_Media_Mensual, Temp_Maxima_Mensual),
fill = "Área T Media - T Max"), alpha = 0.5, data = data_mensual) +
labs(title = "Variación de la temperatura en Madrid (2015-2024)", subtitle = " Linea de temperaturas diarias para visualizar cambios puntuales \ncon areas representando temperaturas mensuales medias",x = "Tiempo en años", y = "Temperatura") +
# Leyenda para las áreas
scale_fill_manual(name = "Áreas",
values = c("Área T Min - T Media" = "blue",
"Área T Media - T Max" = "darkmagenta")) +
# Leyenda para las líneas
scale_color_manual(name = "Líneas",
values = c("T Minima (ºC)" = "green",
"T Media (ºC)" = "yellow",
"T Maxima (ºC)" = "red",
"T Media Mensual (ºC)" = "black")) +
scale_x_date(date_breaks = "1 year", date_labels = "%Y",
minor_breaks = "3 months") +
scale_y_continuous(breaks = seq((floor(min(data$y1) / 10) * 10 - 10), (ceiling(max(data$y3) / 10) * 10 + 10), by = 10)) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"), plot.subtitle = element_text(hjust = 0.5), plot.caption = element_text(hjust = 0, face = "italic") # Estilo del comentario
) +
# Añadir el comentario debajo del gráfico
labs(caption = "Autor: Mario Salvador López Muñoz | Datos: Meteostat") +
guides(color = guide_legend(override.aes = list(size = 5))) +
geom_segment(aes(x = min_tmin_x, y = min_tmin +2, xend = min_tmin_x + 100, yend = min_tmin +2.5),
color = "black") + # Línea hacia arriba
# Añadir etiqueta "Filomena" en un recuadro
geom_label(aes(x = min_tmin_x, y = min_tmin, label = "Filomena"),
fill = "white", color = "black", size = 4,
label.size = 0.5, # Grosor del borde
hjust = -0.2, vjust = -1)
grid.arrange(plot1, plot2, ncol = 1)
# Cargar las librerías necesarias
library(ggplot2)
library(RColorBrewer)
library(waffle)
# Datos para la subdivisión de Chordata
data1 <- data.frame(
Categoria = c('Peces de espinas', 'Anfibios', 'Aves', 'Mamíferos', 'Reptiles', 'Peces cartilaginosos'),
Cantidad = c(159, 84, 83, 37, 32, 1)
)
# Convertir las clases en una forma adecuada
data1$Celdas <- round(data1$Cantidad / sum(data1$Cantidad) * 100)
data1$Celdas[data1$Categoria == "Peces cartilaginosos"] <- data1$Celdas[data1$Categoria == "Peces cartilaginosos"] + 1
# Usar la paleta Set1 de RColorBrewer para los colores
colores_set1 <- c("#4DAF4A", "#FF7F00", "#E41A1C", "#984EA3", "#377EB8", "#A65628", "#984EA3", "#FF69B4")
# Crear el waffle chart con colores de la paleta Set1
waffle_subdivision <- ggplot(data1, aes(fill = Categoria, values = Celdas)) +
geom_waffle(n_rows = 10, color = "white", radius = grid::unit(0.2, "npc"), width = 0.97, height = 0.97) + # Definir filas y color de bordes
scale_fill_manual(values = colores_set1) + # Usar los colores de Set1
labs(title = "Distribución de animales extintos",
x = "Cada cuadrado representa 1%",
fill = "Clase") + # Cambiar el nombre de la leyenda
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, vjust = -5, face = "bold", size = 16),
plot.subtitle = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0.2, vjust = 10, face = "italic"),
legend.position = "right", # Posición de la leyenda
axis.title.y = element_blank(), # Eliminar el label del eje Y
axis.title.x = element_text(vjust = 8), #Mueve la posicion del titulo del eje x
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank()) +
labs(caption = "Autor: Mario Salvador López Muñoz | Datos: IUCN Red List of Threatened Species. Version 2024-1")
print(waffle_subdivision)
library(ggplot2)
oecd_data <- data.frame(
paises = c('Alemania', 'Arabia Saudita', 'Argentina', 'Australia', 'Brasil', 'Canadá', 'China', 'Corea del Sur', 'Estados Unidos', 'Francia', 'India', 'Indonesia', 'Italia', 'Japón', 'México', 'Reino Unido', 'Rusia', 'Sudáfrica', 'Türkiye'),
pib_percapita = c(49520, 30686, 12959, 64440, 9899, 49419, 11657, 30594, 76864, 41330, 2338, 4612, 36070, 31111, 13070, 46619, 12922, 5764, 12080),
origen_datos = "https://datosmacro.expansion.com/paises/grupos/g20"
)
# Calcular la media del PIB per cápita
media_pib <- mean(oecd_data$pib_percapita)
# Crear la columna 'pib_percapita_respecto' con respecto a la media
oecd_data$pib_percapita_respecto <- oecd_data$pib_percapita - media_pib
# Ordenar los países por PIB per cápita
oecd_data <- oecd_data[order(oecd_data$pib_percapita), ]
#Grafico
ggplot(oecd_data, aes(x = reorder(paises, pib_percapita), y = pib_percapita - 28500, fill = pib_percapita > 28500)) +
geom_bar(stat = "identity", width = 0.7) +
coord_flip() +
scale_y_continuous(breaks = seq(-13500, 50000, by = 13500), labels = function(x) x + 28500) +
scale_fill_manual(values = c("#FC4E2A", "#1A9850"), labels = c("Negativo", "Positivo")) +
labs(
title = "Diferencias en el PIB per capita de paises del G20",
subtitle = "La media del PIB per capita es 28.500€",
x = "Paises",
y = "Diferencia ( €)",
fill = "Crecimiento"
) +
theme_minimal() +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.3, face = "bold", size = 16),
plot.subtitle = element_text(hjust = 0.35),
plot.caption = element_text(hjust = -0.3, vjust = 2, face = "italic")
) +
labs(caption = "Autor: Mario Salvador López Muñoz | Datos: https://datosmacro.expansion.com/paises/grupos/g20")
knitr::include_url("https://datawrapper.dwcdn.net/E882I/1/", height = 800)import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator
df = pd.read_csv('~/Desktop/MasterBigData/0_Visualizacion/Actividad/Datos/Road Accident Data.csv')
df['Accident_Severity'].replace('Fetal', 'Fatal', inplace=True)<string>:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.df_filtrado = df[['Day_of_Week', 'Accident_Severity']]
df_grouped = df_filtrado.groupby(['Day_of_Week', 'Accident_Severity']).size().unstack(fill_value=0)
df_grouped_percentage = df_grouped.div(df_grouped.sum(axis=1), axis=0) * 100
df_grouped_percentage.index = ['Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes', 'Sabado', 'Domingo']
df_grouped_percentage.columns = ['Muy Grave', 'Severo', 'Leve']
severity_order = ['Leve', 'Severo', 'Muy Grave']
colors = ['#6B8E23', '#FF8C00', '#ff0000']
df_grouped_percentage = df_grouped_percentage[severity_order]
ax = df_grouped_percentage.plot(kind='bar', stacked=True, figsize=(10, 6), color=colors)
#Yo diria que en este caso si que se puede hacer zoom, ya que estamos viendo las diferencias que hay entre las columnas. Por debajo de 75% es innecesario y no lleva a confusión
plt.ylim(60, 105)(60.0, 105.0)ax.yaxis.grid(True)
ax.yaxis.set_minor_locator(AutoMinorLocator(n=3))
ax.grid(which='minor', linestyle=':', linewidth='0.5', color='gray')
plt.xlabel('Dia de la Semana')
plt.ylabel('Accidentes en porcentaje')
plt.title('Severidad de Accidentes de trafico por Dia de la semana')
plt.legend(title='Severity', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout() # Ajusta automáticamente para optimizar el espacio
plt.show()
knitr::include_url("https://datawrapper.dwcdn.net/xAvEf/1/", height = 800)knitr::include_url("https://datawrapper.dwcdn.net/IFeZG/1/", height = 800)knitr::include_url("https://datawrapper.dwcdn.net/ZNKBp/1/", height = 800)import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data = {
"category": ["Education", "Entertainment", "Games", "Others", "Photo & Video"],
"free": [132, 334, 2257, 1166, 167],
"paid": [321, 201, 1605, 832, 182],
"total": [453, 535, 3862, 1998, 349],
"paid_per": [70.860927, 37.570093, 41.558778, 41.641642, 52.148997],
"free_per": [29.139073, 62.429907, 58.441222, 58.358358, 47.851003]
}
df = pd.DataFrame(data)
list_free = df.free.tolist()
list_paid = df.paid.tolist()
sns.set(style="whitegrid")
plt.figure(figsize=(15, 8))
N = len(df)
ind = np.arange(N)
width = 0.56
p1 = plt.bar(ind, list_free, width, color='#FF6347', label='free')
p2 = plt.bar(ind, list_paid, width, bottom=list_free, color='#4682B4', label='paid')
plt.xticks(ind, df['category'])([<matplotlib.axis.XTick object at 0x12ffcf200>, <matplotlib.axis.XTick object at 0x12df3d130>, <matplotlib.axis.XTick object at 0x1389aa3c0>, <matplotlib.axis.XTick object at 0x12ffcdee0>, <matplotlib.axis.XTick object at 0x12ffba240>], [Text(0, 0, 'Education'), Text(1, 0, 'Entertainment'), Text(2, 0, 'Games'), Text(3, 0, 'Others'), Text(4, 0, 'Photo & Video')])plt.legend()
plt.title("Distribución de Aplicaciones Gratis y de Pago por Categoría")
plt.ylabel("Numero de Aplicaciones")
plt.figtext(0.5, -0.05, "Gráfico: Mario Salvador López Muñoz - Fuente: Editado a partir de original en Kaggle",
wrap=True, horizontalalignment='center', fontsize=10, style='italic', color='gray')
plt.show()
knitr::include_url("https://datawrapper.dwcdn.net/5TYfl/1/", height = 800)library(circlize)========================================
circlize version 0.4.16
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/
If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
in R. Bioinformatics 2014.
This message can be suppressed by:
suppressPackageStartupMessages(library(circlize))
========================================df <- read.csv("https://raw.githubusercontent.com/apalbright/Friends/master/raw_data/friendsdata.csv")
friends <- combn(c("Chandler", "Joey", "Monica", "Phoebe", "Rachel", "Ross"), 2)
relations <- data.frame(from = friends[1, ], to = friends[2, ])
total <- list()
dyads <- c("12", "13", "14", "15", "16", "23", "24", "25", "26", "34", "35", "36", "45", "46", "56")
for (x in dyads) {
total[x] = nrow(df[df$dynamics == x, ])
}
relations["Total"] = as.numeric(unlist(total))
colors <- c("Chandler" = "tomato",
"Joey" = "goldenrod",
"Monica" = "steelblue",
"Phoebe" = "slateblue",
"Rachel" = "orchid",
"Ross" = "mediumseagreen")
chordDiagram(relations, grid.col = colors)
title("Relaciones entre los personajes de Friends")
mtext("Autor: Mario Salvador López Muñoz | Datos: https://github.com/apalbright/Friends",
side = 1, line = 4, adj = 1, cex = 0.7, col = "gray50")
library(grid)
library(pheatmap)
# Cargar el dataset y crear la matriz
x <- data.matrix(UScitiesD, rownames.force = TRUE)
# Crear el heatmap
pheatmap(
x,
color = heat.colors(256),
fontsize_row = 10, # Tamaño de las etiquetas de las filas
fontsize_col = 10, # Tamaño de las etiquetas de las columnas
angle_col = 45, # Inclinación de las etiquetas de las columnas
cellwidth = 20, cellheight = 20, # Ajuste de tamaño de las celdas
border_color = NA, # Sin bordes para una apariencia más clara
display_numbers = FALSE, # Opcional: muestra o no los valores numéricos
cluster_cols = FALSE
)
# Agregar el título y el subtítulo usando grid.text
grid.text("Distancias entre Ciudades de EE. UU. expresadas en millas",
y = unit(1, "npc") - unit(1, "lines"),
gp = gpar(fontsize = 14, fontface = "bold"))
grid.text("Autor: Mario Salvador López Muñoz | Datos: UScitiesD (data de R)",
y = 0.05,
gp = gpar(fontsize = 10))
wars_data <- data.frame(
war = c("Guerra de los Treinta Años", "Guerra de Sucesión Española", "Guerras Napoleónicas", "Primera Guerra Mundial", "Segunda Guerra Mundial", "Guerra de los Siete Años", "Guerra Franco-Prusiana", "Guerra Civil Española", "Guerra de Bosnia", "Guerra de Kosovo", "Guerra de Crimea", "Guerra de los Diez Años", "Guerra de los Balcanes", "Guerra de Irak", "Guerra de Ucrania"),
start_year = c(1618, 1701, 1803, 1914, 1939, 1756, 1870, 1936, 1992, 1998, 1853, 1947, 1912, 2003, 2022),
end_year = c(1648, 1714, 1815, 1918, 1945, 1763, 1871, 1939, 1995, 1999, 1856, 1954, 1913, 2011, NA) # 'NA' para conflictos en curso
)
library(ggplot2)
# Calcular la duración de cada guerra
wars_data$duration <- wars_data$end_year - wars_data$start_year
# Crear el gráfico
ggplot(wars_data, aes(x = start_year, y = duration, label = war)) +
geom_segment(aes(xend = end_year, yend = duration), size = 3, color = "blue") +
geom_text(aes(y = duration, label = war), vjust = -0.5) +
labs(title = "Duración de Guerras en Europa",
x = "Año",
y = "Duración (años)") +
theme_minimal()Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_segment()`).Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).
library(dplyr)
library(lubridate)
# Crear un dataset de ejemplo
set.seed(123)
time_data <- data.frame(
fecha = seq(ymd("2020-01-01"), ymd("2023-01-01"), by = "month"),
temperatura = rnorm(37, mean = 15, sd = 5), # Temperatura aleatoria
precipitacion = rnorm(37, mean = 50, sd = 20) # Precipitación aleatoria
)
library(ggplot2)
# Gráfico de líneas para temperatura y precipitación
ggplot(time_data, aes(x = fecha)) +
geom_line(aes(y = temperatura, color = "Temperatura (°C)"), size = 1) +
geom_line(aes(y = precipitacion / 2, color = "Precipitación (mm)"), size = 1) + # Ajustar escala
scale_y_continuous(sec.axis = sec_axis(~ .*2, name = "Precipitación (mm)")) + # Eje secundario
labs(title = "Variación de Temperatura y Precipitación a lo Largo del Tiempo",
x = "Fecha",
y = "Temperatura (°C)",
color = "Variables") +
theme_minimal()
library(igraph)
Attaching package: 'igraph'The following object is masked from 'package:circlize':
degreeThe following objects are masked from 'package:lubridate':
%--%, unionThe following objects are masked from 'package:purrr':
compose, simplifyThe following object is masked from 'package:tidyr':
crossingThe following object is masked from 'package:tibble':
as_data_frameThe following objects are masked from 'package:dplyr':
as_data_frame, groups, unionThe following objects are masked from 'package:stats':
decompose, spectrumThe following object is masked from 'package:base':
unionlibrary(networkD3)
library(RColorBrewer)
# Cargar el dataset
df <- read.csv('Datos/Family Guy Dataset.csv')
# Definir el diccionario de normalización
normalization_dict <- list(
"Peter" = "Peter Griffin",
"Brian" = "Brian",
"Chris" = "Chris",
"Stewie" = "Stewie",
"Lois" = "Lois",
"Meg" = "Meg",
"Cleveland" = "Cleveland",
"Quagmire" = "Quagmire",
"Joe" = "Joe Swanson",
"Carter" = "Carter Pewterschmidt",
"Mayor Adam West" = "Mayor Adam West",
"Chicken" = "The Giant Chicken",
"Herbert" = "Herbert",
"Mort Goldman" = "Mort Goldman"
)
# Función para normalizar nombres de personajes
normalize_characters <- function(characters, norm_dict) {
normalized <- sapply(characters, function(character) {
character <- trimws(character)
for (key in names(norm_dict)) {
if (grepl(key, character, fixed = TRUE)) {
return(norm_dict[[key]])
}
}
return(character)
})
return(normalized)
}
# Inicializar un data frame vacío para almacenar las conexiones
connections_df <- data.frame(from = character(), to = character(), peso = integer(), stringsAsFactors = FALSE)
# Crear el DataFrame de conexiones
for (i in 1:nrow(df)) {
if (!is.na(df$Featuring[i])) {
characters <- unlist(strsplit(as.character(df$Featuring[i]), ","))
normalized_characters <- normalize_characters(characters, normalization_dict)
if (length(normalized_characters) > 1) { # Solo procesar si hay más de un personaje
combs <- combn(normalized_characters, 2)
for (j in 1:ncol(combs)) {
connections_df <- rbind(connections_df, data.frame(from = combs[1, j], to = combs[2, j], peso = 1))
}
}
}
}
# Contar las conexiones
links <- aggregate(peso ~ from + to, data = connections_df, FUN = sum)
# Crear la red
network <- graph_from_data_frame(d = links, directed = FALSE)
# Contar el grado de cada nodo
deg <- degree(network, mode = "all")
# Filtrar nodos para mantener solo los 20 más conectados
top_nodes <- names(sort(deg, decreasing = TRUE)[1:15])
filtered_links <- links[links$from %in% top_nodes & links$to %in% top_nodes,]
#Cambiamos el color de los links. Normalizamos valores, escojemos paleta aplicamos colores
filtered_links <- filtered_links %>%
mutate(normalized_value = (peso - min(peso)) / (max(peso) - min(peso)))
palette <- colorRampPalette(brewer.pal(9, "Blues"))
filtered_links <- filtered_links %>%
mutate(linkColor = palette(100)[as.numeric(cut(normalized_value, breaks = 100))])
p <- simpleNetwork(filtered_links, height = "500px", width = "700px",
Source = 1,
Target = 2,
linkDistance = 300,
charge = -200,
fontSize = 20,
fontFamily = "Arial",
linkColour = filtered_links$linkColor,
nodeColour = "#FFA500",
opacity = 0.9,
zoom = TRUE)
p#No veas el curro que tiene esto... Y despues para que la unica diferencia es poder poner titulo... Aun se pueden mejorar algunas cosas, pero voy a seguir con otros graficos...
library(tidyverse)
library(treemap)
library(ggfittext)
library(scales)
Attaching package: 'scales'The following object is masked from 'package:purrr':
discardThe following object is masked from 'package:readr':
col_factorlibrary(ggtext)
library(readr)
library(dplyr)
library(ggplotify)
DatosADB <- read_delim("Datos/DatosADB.csv", delim = ";",
escape_double = FALSE, trim_ws = TRUE)Rows: 136822 Columns: 11── Column specification ────────────────────────────────────────────────────────
Delimiter: ";"
chr (8): ID, Season, Province, District, Date, Land, Description, Stage
dbl (1): Code
num (2): Latitude, Longitude
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.filtered_DatosADB <- DatosADB %>%
mutate(Stage = ifelse(Stage == "initial stage with canopy cover 20%", "Initial", Stage)) %>%
filter(Stage %in% c("Peak Growth", "Sowing", "Harvested", "Initial")) %>%
select(Stage, Description)
grouped_DatosADB <- filtered_DatosADB %>%
group_by(Stage, Description) %>%
summarize(Valores = n(), .groups = 'drop') %>%
arrange(desc(Valores))
grouped_DatosADB$Description <- ifelse(grouped_DatosADB$Description == "Maize (Corn)", "Maize", grouped_DatosADB$Description)
top_30 <- head(grouped_DatosADB, 30)
# treemap
data_tree <- treemap(top_30,
index=c("Stage","Description"),
vSize="Valores",
type="index")
data_ggplot <- data_tree[["tm"]] %>%
as_tibble() %>%
arrange(desc(vSize)) %>%
mutate(rank = row_number(),
xmax = x0 + w,
ymax = y0 + h
)
data_ggplot_2 <- data_ggplot %>%
group_by(Stage) %>%
filter(vSize == max(vSize)) %>%
ungroup()
data_ggplot_3 <- data_ggplot %>%
filter(!is.na(Description)) %>% # Filtrar filas donde Description no es NA
mutate(Description = as.character(Description))%>%
ungroup() %>%
mutate(size = 3*w * (10 / nchar(Description)))
min_size <- 1.5 # Define el tamaño mínimo deseado
p1 <- ggplot() + # Inicializa ggplot sin un dataframe
geom_rect(data = data_ggplot_2, # Primer conjunto de datos
aes(xmin = x0,
ymin = y0,
xmax = xmax,
ymax = ymax,
fill = color),
size = 2,
colour = "#1E1D23",
alpha = 0.5) + # Transparencia para el primer conjunto
geom_text(data = data_ggplot_2, # Añadir texto del primer conjunto de datos
aes(x = (x0 + xmax) / 2,
y = (y0 + ymax) / 2,
label = Stage),
color = "black",
size = 3,
fontface = "bold") +
# Añadir el segundo geom_rect con un segundo conjunto de datos
geom_rect(data = data_ggplot_3, # Segundo conjunto de datos
aes(xmin = x0,
ymin = y0,
xmax = xmax,
ymax = ymax,
fill = color),
size = 0.5,
colour = "#1E1D23",
alpha = 0.3) + # Transparencia para el segundo conjunto
geom_text(data = data_ggplot_3, # Añadir texto del segundo conjunto de datos
aes(x = (x0 + xmax) / 2,
y = (y0 + ymax) / 2,
size = size,
label = Description),
color = "black",
fontface = "bold") +
labs(title = "Cosechas de Pakistán",
subtitle = "Tamaño del área equivale a la cantidad de plantaciones",
caption = "Data: ADB - https://data.adb.org/dataset/ground-truthing-survey-data-crop-type-pakistan-rabi-2022-kharif-2023 | Design: Mario Salvador López") +
theme_void() +
theme(legend.position = "none") +
theme(panel.background = element_rect(fill = "transparent", color = NA), # Fondo transparente
plot.background = element_rect(fill = "transparent", color = NA)) # Fondo transparente para todo el gráfico
p1
# Paquetes necesarios
#Me he apoyado en este enlace para hacer el grafico: https://github.com/nrennie/30DayChartChallenge/blob/main/2024/
library(ggplot2)
library(dplyr)
library(showtext)Loading required package: sysfontsLoading required package: showtextdblibrary(ggforce)
library(tibble)
# Datos de los eventos históricos
events <- data.frame(
event = c("Mammals", "Dinosaurs", "Coal Swamps", "Land Plants",
"Trilobites", "Jellyfish", "Seaweeds", "Sexual\nReproduction",
"Single-Celled Algae", "Iron-\nFormations",
"Oldest Fossils", "Origin of Life", "Meteorite\nBombardment",
"Formation of\nthe Earth"),
time = c("23:39", "22:56", "22:24", "21:52", "21:04",
"20:48", "20:28", "18:08", "14:08", "06:00", "05:36",
"04:00", "03:00", "00:00:00")
)
# Función para convertir el tiempo en ángulos
time_to_angle <- function(time_str) {
parts <- as.numeric(unlist(strsplit(time_str, ":")))
hours <- parts[1]
minutes <- parts[2]
seconds <- ifelse(length(parts) == 3, parts[3], 0)
total_seconds <- hours * 3600 + minutes * 60 + seconds
angle <- (total_seconds / (24 * 3600)) * 360
return(angle)
}
# Convertir eventos a ángulos y agregar etiquetas
events <- events %>%
mutate(angle = sapply(time, time_to_angle))
events <- events %>%
mutate(
x = 1.3 * cos((90 - angle) * pi / 180), # Convertir ángulo a radianes
y = 1.3 * sin((90 - angle) * pi / 180), # Convertir ángulo a radianes
x_linea = 1 * cos((90 - angle) * pi / 180),
y_linea = 1 * sin((90 - angle) * pi / 180)
)
# Datos del diseño de reloj
r <- 1.1
theta <- seq(0, (2 * pi), length.out = 13)[1:12]
clock_data <- tibble(
x = r * cos(theta),
y = r * sin(theta),
angle = 90 + 360 * (theta / (2 * pi)),
label = c("III", "II", "I", "XII", "XI", "X", "IX", "VIII", "VII", "VI", "V", "IV")
)
theta2 <- seq(0, (2 * pi), length.out = 61)[1:60]
clock_data2 <- tibble(
x = r * cos(theta2),
y = r * sin(theta2)
)
# Configuración de fuentes y colores
font_add_google("Roboto", "roboto")
font_add_google("Roboto Slab", "roboto_slab")
showtext_auto()
bg_col <- "#fafafa"
text_col <- "grey10"
body_font <- "roboto"
title_font <- "roboto_slab"
# Crear el gráfico
ggplot() +
geom_point(
data = clock_data2,
mapping = aes(x = x, y = y),
size = 0.5,
colour = text_col
) +
geom_label(
data = clock_data,
mapping = aes(x = x, y = y, label = label),
family = title_font,
size = 8,
label.size = 0,
fill = bg_col,
colour = text_col,
fontface = "bold"
) +
geom_label(
data = events,
mapping = aes(x = x, y = y, label = event),
family = title_font,
size = 3,
label.size = 0,
fill = bg_col,
colour = text_col,
fontface = "bold"
) +
annotate("text", x = -0.00839848, y = 1.499976,
label = "Humanity", size = 3,
family = body_font, colour = text_col,
fontface = "bold"
) +
geom_segment(
data = events,
aes(x = 0, y = 0, xend = x_linea, yend = y_linea),
color = "grey50", # Color de las líneas
linewidth = 0.5 # Grosor de las líneas
) +
geom_segment(
aes(x = 0, y = 0, xend = -7.839396e-03, yend = 1.3999781),
colour = "grey50", # Cambia el color de la línea según lo desees
linewidth = 0.5 # Ajusta el grosor de la línea si es necesario
) +
scale_fill_manual(
values = c("gray80", "gray50", "gray20")
) +
labs(
title = "Historia de la Tierra",
subtitle = "Si la historia de la tierra estuviese comprimda en 12 horas, la humanidad solo\n habria estado presente durante 39 segundos, los dinosaurios 30 minutos.",
caption = "**Graphic**: Mario Salvador López Muñoz. Editado de: https://flowingdata.com/2012/10/09/history-of-earth-in-24-hour-clock/"
) +
coord_fixed() +
theme_void(base_size = 24, base_family = body_font) +
theme(
legend.position = "none",
plot.background = element_rect(fill = bg_col, colour = bg_col),
panel.background = element_rect(fill = bg_col, colour = bg_col),
plot.title = element_text(family = title_font, colour = text_col, size = 12, face = "bold", hjust = 0.5),
plot.subtitle = element_text(family = body_font, colour = text_col, size =10, hjust = 0.5),
plot.caption = element_text(family = body_font, colour = text_col, size = 8, hjust = 0.5)
)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.spatial.distance import pdist, squareform
# Cargar el conjunto de datos
dataset = pd.read_csv("Datos/global-data-on-sustainable-energy (1).csv")
# Seleccionar columnas específicas
filtered_dataset = dataset[["Entity", "Year", "Renewable-electricity-generating-capacity-per-capita"]]
# Filtrar los datos para eliminar filas con valores NaN
filtered_dataset = filtered_dataset.dropna(subset=["Renewable-electricity-generating-capacity-per-capita"])
train_transpuesto = filtered_dataset.transpose()
train_transpuesto = filtered_dataset.pivot(index='Entity', columns='Year', values='Renewable-electricity-generating-capacity-per-capita')
distance_matrix = pd.DataFrame(squareform(pdist(train_transpuesto, metric='euclidean')),
index=train_transpuesto.index, columns=train_transpuesto.index)
plt.figure(figsize=(10, 8))
sns.heatmap(distance_matrix, cmap='coolwarm', annot=False)
plt.title('Matriz de Distancia (Heatmap)')
plt.show()
library(readr)
library(dplyr)
library(ggplot2)
library(ggrepel)
green_data <- read_csv("Datos/global-data-on-sustainable-energy (1).csv")Rows: 3649 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Entity
dbl (19): Year, Access to electricity (% of population), Access to clean fue...
num (1): Density\n(P/Km2)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.filtered_green_data <- green_data %>%
filter(!is.na(green_data$`Renewable-electricity-generating-capacity-per-capita`)) %>%
select(Entity, Year, 'Renewable-electricity-generating-capacity-per-capita')
total_capacity <- filtered_green_data %>%
group_by(Entity) %>%
summarise(total_renewable_capacity = sum(`Renewable-electricity-generating-capacity-per-capita`, na.rm = TRUE)) %>%
arrange(desc(total_renewable_capacity)) # Ordenar de mayor a menor
top_7_entities <- total_capacity %>%
slice_head(n = 7)
final_green_data <- filtered_green_data %>%
filter(Entity %in% top_7_entities$Entity)
# Crear el gráfico de líneas
line_plot <- ggplot(final_green_data, aes(x = Year, y = `Renewable-electricity-generating-capacity-per-capita`, color = Entity, group = Entity)) +
geom_line(size = 1) + # Dibujar líneas
geom_text_repel(data = final_green_data %>% group_by(Entity) %>% summarise(last_year = max(Year), last_value = last(`Renewable-electricity-generating-capacity-per-capita`)),
aes(x = last_year, y = last_value, label = Entity),
vjust = 0.5,
hjust = 0,
nudge_x = 3,
size = 3) +
labs(title = "Paises con mayor Generación de Electricidad Renovable per cápita del mundo",
x = "Año",
y = "Capacidad Renovable de Electricidad por Persona (kW/hab)",
caption = "**Graphic**: Mario Salvador López Muñoz. Datos: https://www.kaggle.com/datasets/anshtanwar/global-data-on-sustainable-energy") +
theme_minimal() +
theme(legend.position = "none")
print(line_plot)
library(readr)
library(dplyr)
library(tidyr)
tourist_data <- read_delim("Datos/International tourist arrivals new.csv",
delim = ";", escape_double = FALSE, trim_ws = TRUE)Rows: 189 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ";"
chr (1): Entity
dbl (2): Year, International tourist arrivals by region
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.tourist_data_wide <- tourist_data %>%
pivot_wider(
names_from = Year, # Cada año se convierte en una columna
values_from = `International tourist arrivals by region` # Los valores de cada año
)
write.csv(tourist_data_wide, "Datos/tourist_data_ready.csv", row.names = FALSE)knitr::include_url("https://datawrapper.dwcdn.net/7hfXe/1/", height = 800)knitr::include_url("https://datawrapper.dwcdn.net/gRfjh/1/", height = 800)library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(viridis)Loading required package: viridisLite
Attaching package: 'viridis'The following object is masked from 'package:scales':
viridis_pallibrary(ggExtra)
pobreza <- read_csv("Datos/SDG_0111_SEX_AGE_RT_A-20241104T2221.csv")Rows: 38085 Columns: 7── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): ref_area.label, source.label, indicator.label, sex.label, classif1....
dbl (2): time, obs_value
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.filtered_pobreza <- pobreza %>%
filter(sex.label == "Sex: Total") %>%
filter(classif1.label == "Age (Youth, adults): 15+") %>%
select(ref_area.label, sex.label, time, obs_value)
filtered_pobreza_2 <- filtered_pobreza %>%
filter(!grepl("Upper-middle|Lower-middle|Low income|High income|World|Asia|America|Southern|Northern|Eastern|Western|APEC|G20|BRICS|ASEAN|MENA|CARICOM", ref_area.label))
pobreza_2001 <- filtered_pobreza_2 %>%
filter(time == 2001) %>%
select(ref_area.label, obs_value) %>%
arrange(obs_value) # Ordena de mayor a menor
paises_ordenados <- pobreza_2001$ref_area.label
ordered_pobreza <- filtered_pobreza_2 %>%
mutate(ref_area.label = factor(ref_area.label, levels = paises_ordenados)) %>%
arrange(ref_area.label) # Ahora ordena el dataframe original según el nuevo factor
lista_paises <- unique(filtered_pobreza_2$ref_area.label)library(viridis)
library(ggExtra)
p <-ggplot(ordered_pobreza, aes(time,ref_area.label,fill=obs_value)) +
geom_tile(color= "white",size=0.1) +
scale_fill_viridis(name="Porcentaje",option ="C")
#p <-p + facet_grid(year~month)
p <-p + scale_y_discrete(breaks = unique(filtered_pobreza$ref_area.label))
p <-p + scale_x_continuous()
p <-p + theme_minimal(base_size = 8)
p <-p + labs(title= paste("Porcentaje de la poblacion trabajadora que vive\n con menos de 2.15$ al dia"), x="Año", y="")
p <-p + theme(legend.position = "bottom")+
theme(plot.title=element_text(size = 14))+
theme(axis.text.y=element_text(size=6)) +
theme(strip.background = element_rect(colour="white"))+
theme(plot.title=element_text(hjust=0))+
theme(axis.ticks=element_blank())+
theme(axis.text=element_text(size=7))+
theme(legend.title=element_text(size=8))+
theme(legend.text=element_text(size=6))+
labs(caption = "Graphic: Mario Salvador López Muñoz. Datos: https://ilostat.ilo.org/topics/working-poverty/") +
removeGrid()#ggExtra
p
#Importación de los datos
data <- read_csv("Datos/export_Datos_Clima_Madrid.csv")Rows: 3318 Columns: 11
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (8): tavg, tmin, tmax, prcp, snow, wdir, wspd, pres
lgl (2): wpgt, tsun
date (1): date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data$date <- as.Date(data$date, format="%Y-%m")
data_temp <- data %>% mutate(year = year(date),
month = month(date, label=TRUE),
day = day(date))
data_temp <-data_temp %>% select(day,month,year,tavg)%>%
fill(tavg)%>%
filter(year != 2014)
str(data_temp)tibble [3,287 × 4] (S3: tbl_df/tbl/data.frame)
$ day : int [1:3287] 1 2 3 4 5 6 7 8 9 10 ...
$ month: Ord.factor w/ 12 levels "ene"<"feb"<"mar"<..: 1 1 1 1 1 1 1 1 1 1 ...
$ year : num [1:3287] 2015 2015 2015 2015 2015 ...
$ tavg : num [1:3287] 5.9 7.6 7.7 10.1 7.7 5.7 6.9 8.2 9.7 8.2 ...data_temp$month <- factor(data_temp$month, levels = c("ene", "feb", "mar", "abr", "may", "jun", "jul", "ago", "sep", "oct", "nov", "dic"), ordered = TRUE)
p <-ggplot(data_temp,aes(month,day,fill=tavg))+
geom_tile(color= "white",size=0.1) +
scale_fill_viridis(name="Temperatura C",option ="C")
p <-p + facet_grid(year~ .)
p <-p + scale_y_reverse(breaks =c(1,10,20,31))
p <-p + scale_x_discrete(aes(x = month))
p <-p + theme_minimal(base_size = 8)
p <-p + labs(title= paste("Lo que no se nota, pero es indudable"), x="Meses", y="Dia")
p <-p + theme(legend.position = "bottom")+
theme(plot.title=element_text(size = 14))+
theme(axis.text.y=element_text(size=6)) +
theme(strip.background = element_rect(colour="white"))+
theme(plot.title=element_text(hjust=0))+
theme(axis.ticks=element_blank())+
theme(axis.text=element_text(size=7))+
theme(legend.title=element_text(size=8))+
theme(legend.text=element_text(size=6))+
labs(caption = "Graphic: Mario Salvador López Muñoz. Adaptado de: https://r-graph-gallery.com/283-the-hourly-heatmap.html. Datos: https://ilostat.ilo.org/topics/working-poverty/") +
removeGrid()
p #awesomeness
knitr::include_graphics("Datos/Diseño/2.png")
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# Datos
data = {
'Alimento': ['Brócoli', 'Zanahorias', 'Patatas fritas', 'Galletas',
'Manzanas', 'Refresco', 'Pescado', 'Hamburguesa',
'Yogur', 'Pastel'],
'Puntuación': [9, 8, 3, 2, 9, 1, 10, 4, 7, 3]
}
df = pd.DataFrame(data)
# Configurar el estilo de seaborn
sns.set(style="whitegrid")
# Crear el gráfico de barras
plt.figure(figsize=(8, 6))
barplot = sns.barplot(x='Puntuación', y='Alimento', data=df, palette='RdYlGn', hue='Puntuación')
# Añadir etiquetas y título
plt.title('Clasificación de Alimentos\n Bueno vs Malo', fontsize=16, fontweight='bold')
plt.xlabel('Puntuación (1: Malo, 10: Bueno)', fontsize=14)
plt.ylabel('Alimento', fontsize=14)
plt.xlim(0, 11) (0.0, 11.0)plt.tight_layout()
plt.show()
import pandas as pd
from pandasql import sqldf
import matplotlib.pylab as plt
import seaborn as sns
articles = pd.read_csv("Datos/articles.csv")
articles['garment_group_name'] = articles['garment_group_name'].replace({
'Woven/Jersey/Knitted mix Baby': 'Jersey',
'Unknown': 'Other',
"Under-, Nightwear": "Under/Nightwear",
"Dresses/Skirts girls": "Dresses/Skirts",
"Dresses Ladies": "Dresses/Skirts"
})
temp = articles.groupby(["garment_group_name"])["product_type_name"].nunique()
df = pd.DataFrame({"Garment Group": temp.index, "Product Types": temp.values})
df = df.sort_values(["Product Types"], ascending=False)
fig, ax = plt.subplots(figsize=(7.5,4), dpi=150)
plt.title("Cantidad de productos para cada tipo de vestimenta", fontsize=16, fontweight='bold')
plt.xlabel('Cantidad de productos distintos', fontsize=14)
plt.ylabel('Tipo de vestimenta', fontsize=14)
s = sns.barplot(y="Garment Group", x="Product Types", data=df, hue = "Garment Group", palette="cubehelix", legend=False)
plt.figtext(0.5, -0.05, "Gráfico: Mario Salvador López Muñoz - Fuente: Filmaffinity - Creado con python y ChatGPT",
wrap=True, horizontalalignment='center', fontsize=5, style='italic', color='gray')
plt.tight_layout()
plt.show()
library(ggradar)
library(tidyverse)
library(scales)
library(showtext)
library(viridis)
data <- as.data.frame(matrix( c(
"Dalmata", 7, 5, 8, 4, 8, 9, # Valores de Dálmata
"Border Collie",10, 9, 8, 9, 4, 10, # Valores de Border Collie
"Boston Terrier",6, 7, 9, 7, 7, 6, # Valores de Boston Terrier
"Schnauzer", 8, 7, 4, 6, 8, 7 # Valores de Schnauzer
), nrow=4, byrow=TRUE))
# Asignar nombres a las filas
colnames(data) <- c("Species", "Inteligencia", "Valentía", "Lealtad", "Habilidades", "Sociabilidad", "Energía")
# Convertir columnas a numérico
data$Inteligencia <- as.numeric(data$Inteligencia)
data$Valentía <- as.numeric(data$Valentía)
data$Lealtad <- as.numeric(data$Lealtad)
data$Habilidades <- as.numeric(data$Habilidades)
data$Sociabilidad <- as.numeric(data$Sociabilidad)
data$Energía <- as.numeric(data$Energía)
# Y así sucesivamente
lcols <- c("#D3D3D3", "#A9A9A9", "#696969", "#404040")
radar <- data %>%
ggradar(
font.radar = "roboto",
grid.label.size = 4,
axis.label.size = 4,
group.point.size = 3,
gridline.min.colour = "gray60",
gridline.mid.colour = "gray60",
gridline.max.colour = "gray60",
group.colours = lcols
)Warning: 'plot.data' contains value(s) > grid.max, data scaled to grid.maxradar <- radar +
theme(
legend.position = c(0.25, 0.26),
legend.justification = c(1, 0),
legend.text = element_text(size = 7, family = "roboto"),
legend.key = element_rect(fill = NA, color = NA),
legend.background = element_blank(),
legend.spacing.y = unit(10, "cm"),
legend.key.size = unit(10, "cm")
)Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.radar <- radar +
labs(title = "Mejores amigos en blanco y negro") +
theme(
plot.background = element_rect(fill = "#fbf9f4", color = "#fbf9f4"),
panel.background = element_rect(fill = "#fbf9f4", color = "#fbf9f4"),
plot.title.position = "plot", # slightly different from default
plot.title = element_text(
family = "sans",
size = 20,
face = "bold",
color = "#000000"
)
)
print(radar)
library(readr)
library(dplyr)
library(tidyr)
library(lubridate)
president <- read_csv("Datos/presidential_general_averages.csv")Rows: 29491 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): candidate, state, party
dbl (5): pct_trend_adjusted, cycle, pct_estimate, hi, lo
date (1): date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.president <- president %>%
filter(cycle == 2020) %>%
select(candidate, date, state, pct_trend_adjusted)
president <- president %>%
group_by(state) %>%
slice_max(order_by = pct_trend_adjusted, n = 1, with_ties = FALSE) %>%
ungroup() %>%
mutate(pct_trend_adjusted = round(pct_trend_adjusted)) %>%
ungroup()
write.csv(president, "president_data.csv", row.names = FALSE)
#Datos de poblacion obtenidos de wikipedia y editados con excelknitr::include_url("https://datawrapper.dwcdn.net/nKD4f/1/", height = 800)